Overview

Five pages, read in order. They explain what computational causality is, the axiom DeepCausality is built on, the problem classes that become tractable once that axiom is in place, the sixteen innovations the project ships to address them, and how the resulting workspace is laid out.

• Why? — what computational causality is, what it does, and where DeepCausality fits in.
• Premise — one working definition: causality as a spacetime-agnostic monadic process.
• Problem — seven categories of problems where dynamic causality is the right tool and conventional tooling is not.
• Innovations — the catalog of sixteen innovations that, taken together, set DeepCausality apart.
• Architecture — a map of the 29-crate workspace: the primary reasoning path, the crate boundaries, and what depends on what.

Once those land, move to Getting started for runnable code or Concepts for the type-level reference.